Transfer Öğrenme ve Komşuluk Bileşen Analizine Dayalı Balgam Yayma Mikroskop Görüntüleri Üzerinden Otomatik Tüberküloz Teşhisi

Yıl 2022, Cilt: 4 Sayı: 2, 236 - 246, 26.10.2022

Ömer Faruk Nasip , Kenan Zengin

https://doi.org/10.46387/bjesr.1160038

Cited By: 1

Öz

Tüberküloz, Mycobacterium tuberculosis bakterisinin neden olduğu, dünya çapında etkili olan ve ölümle sonuçlanabilen bulaşıcı bir hastalıktır. Kişiden alınan balgam örneğinin mikroskop altında incelenmesi hastalığın en yaygın tanı yöntemlerindendir. Ancak bu manuel işlem hataya açık ve zaman alıcıdır. Bu çalışmada, balgam yayma mikroskop görüntüleri kullanılarak otomatik tüberküloz teşhisi için bir model önerilmiştir. Modelde veri setine ait öznitelikler transfer öğrenme yaklaşımı ile Vgg19, ResNet50 ve DenseNet201 kullanılarak elde edilmiştir. Öznitelikler birleştirildikten sonra Komşuluk Bileşen Analizi (KBA) ile ağırlıklandırılıp en iyi olanları seçilmiştir. Seçilen öznitelikler Destek Vektör Makinesi (DVM) ile eğitilmiş, modelin performansı beş kat çapraz doğrulama kullanılarak hesaplanmıştır. Deneysel sonuçlarda %99.03 doğruluk, %99.24 kesinlik, %98.81 duyarlılık, %99.25 özgüllük ve %99.02 f1-skoru elde edilmiştir.

Anahtar Kelimeler

Tüberküloz, Transfer Öğrenme, Komşuluk Bileşen Analizi, Destek Vektör Makinesi

Kaynakça

• WHO, “Tuberculosis, Overview”. (2021). https://www.who.int/health-topics/tuberculosis (Erişim tarihi: Mart 15, 2022).
• R. O. Panicker, K. S. Kalmady, J. Rajan, and M. K. Sabu, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods”, Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 691-699, 2018.
• M. El-Melegy, D. Mohamed, and T. Elmelegy, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using Faster R-CNN, Transfer Learning and Augmentation”, In A. Morales, J. Fierrez, J. Sánchez, and B. Ribeiro (Eds.), Pattern Recognition and Image Analysis, IbPRIA 2019, Lecture Notes in Computer Science, pp. 270-278, Springer, Cham, 2019.
• E. Korot, Z. Guan, D. Ferraz, S. K. Wagner, G. Zhang, X. Liu, L. Faes, N. Pontikos, S. G. Finlayson, H. Khalid, G. Moraes, K. Balaskas, A. K. Denniston, and P. A. Keane, “Code-free deep learning for multi-modality medical image classification”, Nature Machine Intelligence, vol. 3, pp. 288-298, 2021.
• Ö. F. Nasip, and K. Zengin, “Deep learning based bacteria classification”, 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-5, 2018.
• T. Haryanto, I. Wasito, and H. Suhartanto, “Convolutional Neural Network (CNN) for gland images classification”, 11th International Conference on Information & Communication Technology and System (ICTS), pp. 55-60, 2017.
• F. C. Arisgraha, P. Widiyanti and R. Apsari, “Digital detection system design of mycobacterium tuberculosis through extraction of sputum image using neural network method”, Indonesian Journal of Tropical and Infectious Disease, vol. 3, pp. 35-38, 2015.
• E. Priya, and S. Srinivasan, “Automated object and image level classification of TB images using support vector neural network classifier”, Biocybernetics and Biomedical Engineering, vol. 36, no. 4, pp. 670-678, 2016.
• M. Hu, Y. Liu, Y. Zhang, T. Guan, and Y. He, “Automatic detection of tuberculosis bacilli in sputum smear scans based on subgraph classification”, International Conference on Medical Imaging Physics and Engineering (ICMIPE), pp. 1-7, 2019.
• K. S. Mithra, and W. R. Sam Emmanuel, “Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis”, Signal, Image and Video Processing, vol. 13, pp. 1585–1592, 2019.
• R. Dinesh Jackson Samuel, and B. Rajesh Kanna, “Tuberculosis (TB) detection system using deep neural networks”, Neural Computing and Applications, vol. 31, pp. 1533–1545, 2019.
• R. Dinesh Jackson Samuel, and B. Rajesh Kanna, “Cybernetic microbial detection system using transfer learning”, Multimedia Tools and Applications, vol. 79, pp. 5225–5242, 2020.
• A. Rachmad, N. Chamidah, and R. Rulaningtyas, “Mycobacterium tuberculosis images classification based on combining of convolutional neural network and support vector machine”, Communications in Mathematical Biology and Neuroscience, 85, 2020.
• A. U. Ibrahim, E. Guler, M. Guvenir, K. Suer, S. Serte, and M. Ozsoz, “Automated detection of Mycobacterium tuberculosis using transfer learning”, Journal of Infection in Developing Countries, vol. 15, no. 5, 678-686, 2021.
• Kaggle, “Tuberculosis Image Dataset”. (2020). https://www.kaggle.com/datasets/saife245/tuberculosis-image-datasets (Erişim tarihi: Nisan 15, 2022).
• A. Alem, and S. Kumar, “Transfer learning models for land Cover and land use classification in remote sensing image”, Applied Artificial Intelligence, vol. 36, no. 1, 2022.
• C. Ammatmanee, and L. Gan, “Transfer learning for hostel image classification”, Data Technologies and Applications, vol. 56, pp. 44-59, 2022.
• K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, CoRR, abs/1409.1556, 2015.
• K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, arXiv:1512.03385v1, 2015.
• G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017.
• C. Chen, Y. Tsai, F. Chang, and W. Lin, “Ensemble feature selection in medical datasets: Combining filter, wrapper, and embedded feature selection results”, Expert Systems, vol. 37, 2020.
• E. Alhenawi, R. Al-Sayyed, A. Hudaib and S. Mirjalili, “Feature selection methods on gene expression microarray data for cancer classification: A systematic review”, Computers in Biology and Medicine, vol. 140, 2021.
• W. Yang, K. Wang, and W. Zuo, “Neighborhood component feature selection for high-dimensional data”, Journal of Computers, vol. 7, no. 1, pp. 161-168, 2012.
• L. Wang, D. Johnson, and Y. Lin, “Using EEG to detect driving fatigue based on common spatial pattern and support vector machine”, Turkish Journal of Electrical Engineering & Computer Sciences, vol. 29, no. 3, pp. 1429-1444, 2021.
• M. Azimi-Pour, H. Eskandari-Naddaf and A. Pakzad, “Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete”, Construction and Building Materials, vol. 230, 2020.
• S. Kilicarslan, K. Adem, and M. Celik, “Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network”, Medical Hypotheses, vol. 137, 2020.
• MATLAB, V2021b, MathWorks Inc. (2021). https://www.mathworks.com/products/matlab.html (Erişim tarihi: Ekim 15, 2021).